Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds

Heart murmurs are abnormal sounds present in heartbeats, caused by turbulent blood flow through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome. The recordings are captured from multiple locations around the heart. Our participation investigates the effectiveness of selfsupervised learning for murmur detection. We train the layers of a backbone CNN in a self-supervised way with data from both this year's and the 2016 challenge. We use two different augmentations on each training sample, and normalized temperature-scaled cross-entropy loss. We experiment with different augmentations to learn effective phonocardiogram representations. To build the final detectors we train two classification heads, one for each challenge task. We present evaluation results for all combinations of the available augmentations, and for our multipleaugmentation approach. Our team's, Listen2YourHeart, SSL murmur detection classifier received a weighted accuracy score of 0.737 (ranked 13th out of 40 teams) and an outcome identification challenge cost score of 11946 (ranked 7th out of 39 teams) on the hidden test set.Comment: To be published in the proceedings of CinC 2022 (https://cinc.org/). This is a preprint version of the final pape

Efficient combinatorial optimization algorithms for logistic problems

The field of logistics and combinatorial optimization features a wealth of NP-hard problems that are of great practical importance. For this reason it is important that we have efficient algorithms to provide optimal or near-optimal solutions. In this work, we study, compare and develop Sampling-Based Metaheuristics and Exact Methods for logistic problems that are important for their applications in vehicle routing and scheduling. More specifically, we study two Stochastic Combinatorial Optimization Problems (SCOPs) and finally a Combinatorial Optimization Problem using methods related to the field of Metaheuristics, Monte Carlo Sampling, Experimental Algorithmics and Exact Algorithms. For the SCOPs studied, we emphasize studying the impact of approximating the objective function to the quality of the final solution found. We begin by examining Solution Methods for the Orienteering Problem with Stochastic Travel and Service Times (OPSTS). We introduce the state-of-the-art before our contributions and proceed to examining our suggested improvements. The core of our improvements stem from the approximation of the objective function using a combination of Monte Carlo sampling and Analytical methods. We present four new Evaluators (approximations) and discuss their advantages and disadvantages. We then demonstrate experimentally the advantages of the Evaluators over the previous state-of-the-art and explore their trade- offs. We continue by generating large reference datasets and embedding our Evaluators in two Metaheuristics that we use to find realistic near-optimal solutions to OPSTS. We demonstrate that our results are statistically significantly better than the previous state-of-the-art. In the next chapter, we present the 2-stage Capacitated Vehicle Routing Problem with Stochastic Demands inspired by an environmental use case. We propose four different solution approaches based on different approximations of the objective function and use the Ant Colony Metaheuristic to find solutions for the problem. We discuss the trade-offs of each proposed solution and finally argue about its potentially important environmental application. Finally, focus on exact methods for the Sequential Ordering Problem (SOP). Firstly, we make an extensive experimental comparison of two exact algorithms existing in the literature from different domains (cargo and transportation and the other compilers). From the experimental comparison and application of the algorithms in new contexts we were able to close nine previously open instances in the literature and improve seventeen more. It also led to insights for the improvement of one of the methods (The Branch-and-Bound Approach - B&B). We proceed with the presentation of the improved version that led to the closing of eight more instances and speeding up the previous version of the B&B algorithm by 4%-98%

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.eu), a system designed to collect objective behavioral data from children and adolescent populations as well as their environment in order to support public health authorities in formulating effective, context-specific policies and interventions addressing childhood obesity. We present an overview of the data acquisition, indicator extraction, data exploration and analysis components of the BigO system, as well as an account of its preliminary pilot application in 33 schools and 2 clinics in four European countries, involving over 4,200 participants.Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canad

Toward Systems Models for Obesity Prevention: A Big Role for Big Data

The relation among the various causal factors of obesity is not well understood, and there remains a lack of viable data to advance integrated, systems models of its etiology. The collection of big data has begun to allow the exploration of causal associations between behavior, built environment, and obesity-relevant health outcomes. Here, the traditional epidemiologic and emerging big data approaches used in obesity research are compared, describing the research questions, needs, and outcomes of 3 broad research domains: eating behavior, social food environments, and the built environment. Taking tangible steps at the intersection of these domains, the recent European Union project "BigO: Big data against childhood obesity" used a mobile health tool to link objective measurements of health, physical activity, and the built environment. BigO provided learning on the limitations of big data, such as privacy concerns, study sampling, and the balancing of epidemiologic domain expertise with the required technical expertise. Adopting big data approaches will facilitate the exploitation of data concerning obesity-relevant behaviors of a greater variety, which are also processed at speed, facilitated by mobile-based data collection and monitoring systems, citizen science, and artificial intelligence. These approaches will allow the field to expand from causal inference to more complex, systems-level predictive models, stimulating ambitious and effective policy interventions

Μοντελοποίηση και αυτόματη μέτρηση της ανθρώπινης, σχετικής με βρώση συμπεριφοράς

This work proposes models and algorithms that leverage signals collected from wearable sensors to objectively quantify human eating behavior by extracting eating-behavior indicators. We focus on two different sensors that capture different, yet complimentary, aspects of eating behavior and propose new algorithms for each sensor to derive these indicators, focusing mainly on the generalization under different situations such as noisy background and usage during real-life activities. The first device is a prototype multi-modal sensor that captures audio, photoplethysmography, and acceleration signals. We explore some properties of audio signals of chews, such as their fractal dimension, and propose algorithms based on feature extraction and classification with support vector machines, as well as end-to-end approaches with convolutional neural netwroks. We also propose (for the first time in literature) the use of photoplethysmography as a means to detect chewing, both independently and combined with audio. We propose and evaluate different algorithms. The best algorithm uses a support vector machine classifier with spectral features and is also combined in a late-fusion scheme with a respective audio-based algorithm. This late-fusion approach achieves higher effectiveness compared to each sensor individually and can be improved even further by taking into account the energy signal from a belt-mounted three-dimensional accelerometer (F1-score of 0.76 for a leave-one-subject-out experiment on a challenging, real-life dataset). The audio sensor is also used to recognize food attributes such as crispiness. We form a multi-label problem where labels correspond to food attributes and propose recognition algorithms both for individual chews and for chewing bouts. Experiments show that it is possible to generalize these properties to new, unknown (to the training stage) food types with high effectiveness, in some cases (0.92 weighted accuracy per bout for a leave-one-food-type-out experiment). The second sensor continuously measures the weight of the food inside a plate during a meal. Based on the captured weight signal we can derive useful in-meal indicators of eating behavior. We propose two types of algorithms. The first type focuses on identifying moments when additional food is placed on the plate, since these moments change the generally decreasing trend of the captured weight (as food is removed from the plate to be consumed). The effect of adding food is then removed from the captured signal and bites are identified as abrupt weight decrements. The second type of algorithm models the events of a meal with the help of a context-free grammar. Maximum-likelihood is then used to select the most likely parse tree of the meal. The parse tree is an interpretation of the meal structure and events, and is used to derive the in-meal indicators. The context-free grammar and maximum-likelihood--based algorithm is the one who achieves the lowest average (per meal) error rates for almost all indicators (e.g. 24 grams for total food intake, and 1 minute for meal duration).Η παρούσα διατριβή προτείνει μοντέλα και αλγόριθμους οι οποίοι αξιοποιούν σήματα από φορητούς αισθητήρες με στόχο την αντικειμενική ποσοτικοποίηση της ανθρώπινης, σχετικής με βρώση συμπεριφοράς. Εστιάζουμε σε δύο συσκευές οι οποίοι καταγράφουν διαφορετικού τύπου πληροφορία και προτείνουμε αλγόριθμους για κάθε αισθητήρα οι οποίοι εξάγουν συγκεκριμένους συμπεριφορικούς δείκτες, και εστιάζουμε κυρίως στη γενίκευση κάτω από διαφορετικές συνθήκες όπως θορυβώδες περιβάλλον και ρεαλιστικές συνθήκες. Η πρώτη συσκευή είναι ένα πρωτότυπο μοντέλο και περιλαμβάνει μικρόφωνο, και φωτοπλυθησμογράφο τοποθετημένους στο αυτί, και τρισδιάστατο επιταγχυνσιόμετρο τοποθετημένο στη ζώνη. Παρουσιάζεται μία ανάλυση των ήχων μάσησης με έμφαση στη fractal διάστασή τους, και προτείνονται αλγόριθμοι βασισμένοι στην εξαγωγή χαρακτηριστικών και ταξινομητές τύπου support vector machine, καθώς και συνελικτικά νευρωνικά δίκτυα. Επίσης προτείνουμε (για πρώτη φόρα στη βιβλιογραφία) τη χρήση φωτοπλυθησμογράφου για την ανίχνευση μάσησης, τόσο αυτόνομα όσο και σε συνδυασμό με το μικρόφωνο. Προτείνονται αλγόριθμοι ανίχνευσης μάσησης, ο καλύτερος εκ των οποίων χρησιμοποιεί ταξινομητές τύπου support vector machine με χαρακτηριστικά φάσματος, και μπορεί να συνδυαστεί με τον αντίστοιχο αλγόριθμο ήχου, βελτιώνοντας την επίδοση της ανίχνευσης. Επίσης, η χρήση του επιταγχυνσιομέτρου μπορεί να βελτιώσει ακόμα περισσότερο την επίδοση (0.76 F1-score για leave-one-subject-out πειράματα σε ένα μεγάλο, ρεαλιστικό σύνολο δεδομένων). Το μικρόφωνο χρησιμοποιείται επίσης για την αναγνώριση χαρακτηριστικών τροφής όπως πχ τραγανότητα. Εισάγουμε το πρόβλημα ως ένα πρόβλημα πολλαπλών ετικετών όπου κάθε ετικέτα αντιστοιχεί σε ένα χαρακτηριστικό και προτείνουμε αλγόριθμούς αναγνώρισης τόσο σε επίπεδο μασήματος όσο και σε μπουκιάς. Τα πειραματικά αποτελέσματα δείχνουν ότι μπορούμε να γενικεύσουμε τόσο σε νέους χρήστες όσο και σε νέους τύπους τροφής με μεγάλη ακρίβεια σε κάποιες περιπτώσεις (0.92 βεβαρυμμένη ακρίβεια ανά μπουκιά για leave-one-subject-out πείραμα). Η δεύτερη συσκευή καταγράφει το βάρος της τροφής που βρίσκεται μέσα σε ένα πιάτο, καθ’ όλη τη διάρκεια ενός γεύματος. Από τέτοιου τύπου καταγραφές μπορούν να εξαχθούν δείκτες που αφορούν το γεύμα, όπως πχ ο ρυθμός πρόσληψης τροφής. Προτείνουμε δύο αλγόριθμους. Ο πρώτος εντοπίζει χρονικές στιγμές κατά τις οποίες προστίθεται επιπλέον ποσότητα φαγητού, καθώς κατά τις στιγμές αυτές αλλοιώνεται η γενικώς φθίνουσα τάση του σήματος. Στη συνέχεια, το καταγεγραμμένο σήμα επεξεργάζεται ώστε να αναιρεθεί η επίδραση της πρόσθεσης φαγητού στο σήμα, και στη συνέχεια εντοπίζονται μπουκιές από τις μικρές πτώσεις στο καταγραφόμενο βάρος. Ο δεύτερος αλγόριθμος μοντελοποιεί την ανθρώπινη συμπεριφορά κατά τη διάρκεια ενός γεύματος με τη χρήση μίας γραμματικής χωρίς συμφραζόμενα. Κάθε γεύμα αντιστοιχεί σε μία συμβολοσειρά των τελικών συμβόλων της γραμματικής, ενώ κάθε ανθρώπινη δράση (όπως μπουκιά, πρόσθεση φαγητού, κλπ) αντιστοιχεί σε ένα μη-τελικό σύμβολο. Σε κάθε συμβολοσειρά αντιστοιχούν πολλαπλά δένδρα, και προτείνουμε έναν τρόπο εκτίμησης της πιθανοφάνειας κάθε δένδρου ώστε να επιλέξουμε το πιο πιθανοφανές. Από το πιο πιθανοφανές δένδρο εξάγονται οι μπουκιές και στη συνέχεια οι συμπεριφορικοί δείκτες. Ο δεύτερος αλγόριθμος επιτυγχάνει τα χαμηλότερα μέσα απόλυτα σφάλματα ανά δείκτη (σε σχέση και με άλλους αλγόριθμους της βιβλιογραφίας), πχ 24 γραμμάρια για το συνολικό βάρος του γεύματος, και 1 λεπτό για τη συνολική διάρκεια

Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned

The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior. These allow the study of eating behavior outside the laboratory in free-living conditions, without the need for video recordings and laborious manual annotations. In this paper, we present a high-level overview of our recent work on intake monitoring using a smartwatch, as well as methods using an in-ear microphone. We also present evaluation results of these methods in challenging, real-world datasets. Furthermore, we discuss use-cases of such intake monitoring tools for advancing research in eating behavior, for improving dietary monitoring, as well as for developing evidence-based health policies. Our goal is to inform researchers and users of intake monitoring methods regarding (i) the development of new methods based on commercially available devices, (ii) what to expect in terms of effectiveness, and (iii) how these methods can be used in research as well as in practical applications